Abstract: Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users’ preferences, and therefore fails to meet users personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users’ preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations.
Keywords: Recommender system, preference, keyword, big data, MapReduce.
Title: A Personal Recommendation Searcher Using Keywords
Author: Chiranjeevi T N, Vishwanath R H
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), SSN 2348-120X (online)
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